The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Two-Fold Approach to Tackle Fake News
Open accessAbstract
With the revolution and growth of the media industry, and development of new mediums to update citizens with the latest news, in recent years there has been a spurt in the production of articles spreading fake information.Many media channels leverage on the concept of spreading eye-catching malicious news that attracts readers which has been proven to be quite dangerous in most cases.These channels post an exaggerated version of the truth, thus leading to an emerging trend of spreading fake news.To tackle this problem, we propose a two-step solution involving machine learning and block chain.The proposed solution consists of a news verification portal using a two-fold approach, which first detects whether the news article is fake or real leveraging the accuracy of a machine learning algorithm and then verifies the source using human crowd auditors on a block chain platform based on proof-of-stake.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Salvi (2020) posits that the proposed integration and application of state-of-theart technologies, such as machine learning and blockchain, have been suggested as new solutions aimed at enhancing the process of verifying news content, thereby ensuring the reliability and authenticity of information shared by citizen journalists.”
From A study of citizen journalism practices in the Kingdom of Bahrain: insights from the social responsibility theory and news verification tools (2026) · cites this paper as Salvi (2020)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fake news detection using naive Bayes classifier
Granik, M., & Mesyura, V. I. (2017) · IEEE Ukraine Conference on Electrical and Computer Engineering (UKRCON) · doi:10.1109/ukrcon.2017.8100379 · Cited by 583
Detecting Fake News with Machine Learning Method
Aphiwongsophon, S., & Chongstitvatana, P. (2018) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2018.8620051 · Cited by 162
Evaluating machine learning algorithms for fake news detection
Gilda, S. (2017) · Student Conference on Research and Development · Cited by 97
Formal Analysis of a Proof-of-Stake Blockchain
Thin, W. Y. M. M., Dong, N., Bai, G., & Dong, J. (2018) · IEEE International Conference on Engineering of Complex Computer Systems (ICECCS) · doi:10.1109/iceccs2018.2018.00031 · Cited by 81
Blockchain-based Notarization for Social Media
Song, G., Kim, S., Hwang, H., & Lee, K. (2019) · 2019 IEEE International Conference on Consumer Electronics (ICCE) · doi:10.1109/icce.2019.8661978 · Cited by 51
Fake news Detection Using Naive Bayes Classifier
Srivastava, R., & Singh, P. (2022) · Journal of Management and Service Science (JMSS) · doi:10.54060/jmss/002.01.005 · Cited by 49
Tracing the Source of News Based on Blockchain
Shang, W., Liu, M., Lin, W., & Jia, M. (2018) · 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2018.8466516 · Cited by 40
Racsko, P. (2021) · Society and Economy · doi:10.1556/204.2021.00020 · Cited by 7
Metadata
- Authors
- Salvi, H.
- Affiliations
- Dwarkadas J. Sanghvi College of Engineering (IN)
- Corresponding author
- Salvi, Harsh
- ORCID
- Salvi, Harsh
- Venue
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2020-04-30
- Volume
- 8
- Issue
- 4
- Pages
- 347-350
- ISSN
- 2321-9653 (ISSN-L)
- OpenAlex
- W3020497727
- Cited by
- 1
- References
- 8
- Citations by year
- 2026: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal for Research in Applied Science and Engineering Technology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- fake news detection, misinformation, news verification, blockchain, proof-of-stake, human verification, machine learning classification, source verification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Two-Fold Approach to Tackle Fake News. https://app.cochat.ai/papers/10.22214/ijraset.2020.4053
- The paper
- Salvi, H. (2020). A Two-Fold Approach to Tackle Fake News. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2020.4053
